The COVID-19 crisis and factors driving international tourists' preferences for contactless dining services

Purpose The purpose of this study is to integrate the theory of planned behavior and the protection motivation theory to propose a theoretical framework for examining the influence of attitude, perceived behavioral control and subjective norms on international tourists' intentions to select restaurants with contactless dining services (CDSs) as a protective behavior against COVID-19. This study further tested the moderation effects of subjective norms on attitude and perceived behavioral control links with intention. Design/methodology/approach In all, 344 international tourists completed questionnaires via QR-coded Google forms in Phuket Old Town and Patong Beach in Thailand. Data analysis was conducted by SPSS and Smart-PLS (partial least square). Findings Intention to select a restaurant with CDSs was influenced by attitude, perceived behavioral control and subjective norms. Subjective norms had a significant negative moderating effect on attitude and intention links. However, the moderation effect of tourists' subjective norms on the connection between their perceived behavioral control and intention toward restaurant dining was not proved. Practical implications Providing CDSs could be a quick solution to decrease the devastating effect of the COVID-19 pandemic on the restaurant industry. Originality/value This study incorporated CDSs to expand the application of the integrated model of theory of planned behavior and protection motivation theory as a theoretical basis in the restaurant industry to explain how international tourists' behavioral choices may change during the pandemic in Thailand. This study also contributes to the travel risk literature by highlighting the influence of attitude anchored on risk and efficacy beliefs (perceived vulnerability, severity and response efficacy) in predicting protective behavioral intention.

A dependency-based machine learning approach to the identification of research topics: a case in COVID-19 studies

Purpose Previous research concerning automatic extraction of research topics mostly used rule-based or topic modeling methods, which were challenged due to the limited rules, the interpretability issue and the heavy dependence on human judgment. This study aims to address these issues with the proposal of a new method that integrates machine learning models with linguistic features for the identification of research topics. Design/methodology/approach First, dependency relations were used to extract noun phrases from research article texts. Second, the extracted noun phrases were classified into topics and non-topics via machine learning models and linguistic and bibliometric features. Lastly, a trend analysis was performed to identify hot research topics, i.e. topics with increasing popularity. Findings The new method was experimented on a large dataset of COVID-19 research articles and achieved satisfactory results in terms of f-measures, accuracy and AUC values. Hot topics of COVID-19 research were also detected based on the classification results. Originality/value This study demonstrates that information retrieval methods can help researchers gain a better understanding of the latest trends in both COVID-19 and other research areas. The findings are significant to both researchers and policymakers.